Browse > Article
http://dx.doi.org/10.6109/jkiice.2013.17.7.1571

Improvement of Signal-to-Noise Ratio for Speech under Noisy Environment  

Choi, Jae-Seung (Department of Electronic Engineering, Silla University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.17, no.7, 2013 , pp. 1571-1576 More about this Journal
Abstract
This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.
Keywords
SNR improvement algorithm; signal-to-noise ratio; SNR estimation method; background noise;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 S. Tamura and A. Waibel, "Noise reduction using connectionist models", Proc. ICASSP-88, Vol. 1, pp. 553-556, 1988.
2 T. T. Le, J. S. Mason and T. Kitamura, "Characteristics of multi-layer perceptron models in enhancing degraded speech", Proc. ICSLP-94, pp. 1611-1614, 1994.
3 J. S. Choi, "Speaker Recognition using LPC Cepstrum Coefficients and Neural Network", Journal of the Korea Institute of Information and Communication Engineering, Vol. 15, No. 12, pp. 2521-2526, December 2011.   과학기술학회마을   DOI   ScienceOn
4 S. F. Boll, "Suppression of acoustic noise in speech using spectral subtraction", IEEE Trans. Acoust., Speech, Signal Processing, Vol. 27, No. 2, pp. 113-120, 1979.   DOI
5 J. S. Lim and A. V. Oppenheim, "Enhancement and Bandwidth Compression of Noisy Speech," Proc., IEEE, Vol. 67, DEC, 1979.
6 L. J. Griffiths and C. W. Jim, "An alternative approach to linearly constrained adaptive beamforming," IEEE Trans. Antennas Propag, AP-30, 27-34, 1982.
7 B. Widrow, R. John, J. R. Glover, J. M. McCool, J. Kaunitz, C. S. Williams, R. H. Hearn, J. R. Zeidler, E. Dong, R. C. Goodlin, "Adaptive noise cancelling: Principles and applications", Proc. IEEE, Vol. 63, No. 12, pp. 1692-1716, 1975.   DOI   ScienceOn
8 A. A. M. Abushariah, T. S. Gunawan, O. O. Khalifa and M. A. M. Abushariah, "English digits speech recognition system based on Hidden Markov Models", 2010 International Conference on Computer and Communication Engineering, pp. 1-5, May 2010.
9 L. Yang, L. Jing, Y. Yuxiang and W. Jian, "Improvement algorithm of DTW on isolated-word recognition", 2011 IEEE International Conference on Computer Science and Automation Engineering, Vol. 3, pp. 319-322, 2011.
10 J. S. Lim, "Speech Enhancement", Prentice-Hall, Inc., Englewood Cliffs, N. J., 1983.
11 H. Hirsch and D. Pearce, "The AURORA experimental framework for the performance evaluations of speech recognition systems under noisy conditions", in Proc. ISCA ITRW ASR2000 on Automatic Speech Recognition: Challenges for the Next Millennium, Paris, France, 2000.